Understanding the accuracy of statistical haplotype inference with sequence data of known phase
Open accessAbstract
Statistical methods for haplotype inference from multi‐site genotypes of unrelated individuals have important application in association studies and population genetics. Understanding the factors that affect the accuracy of this inference is important, but their assessment has been restricted by the limited availability of biological data with known phase. We created hybrid cell lines monosomic for human chromosome 19 and produced single‐chromosome complete sequences of a 48 kb genomic region in 39 individuals of African American (AA) and European American (EA) origin. We employ these phase‐known genotypes and coalescent simulations to assess the accuracy of statistical haplotype reconstruction by several algorithms. Accuracy of phase inference was considerably low in our biological data even for regions as short as 25–50 kb, suggesting that caution is needed when analyzing reconstructed haplotypes. Moreover, the reliability of estimated confidence in phase inference is not high enough to allow for a reliable incorporation of site‐specific uncertainty information in subsequent analyses. We show that, in samples of certain mixed ancestry (AA and EA populations), the most accurate haplotypes are probably obtained when increasing sample size by considering the largest, pooled sample, despite the hypothetical problems associated with pooling across those heterogeneous samples. Strategies to improve confidence in reconstructed haplotypes, and realistic alternatives to the analysis of inferred haplotypes, are discussed. Genet. Epidemiol. © 2007 Wiley‐Liss, Inc.
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Obtaining phase from sequence data alone is not trivial.
“Thus, leveraging phase information in an association study of rare variants may be crucial, but obtaining phase from sequence data alone is not trivial.24, 91–93”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [91]
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- Authors
- Andrés, A. M., Clark, A. G., Shimmin, L., Boerwinkle, E., Sing, C. F., & Hixson, J. E.
- Affiliations
- National Institutes of Health (US)
- Cornell University (US)
- The University of Texas Health Science Center at Houston (US)
- University of Michigan (US)
- Corresponding author
- Andrés, Aida M.
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2007-10-05
- Volume
- 31
- Issue
- 7
- Pages
- 659-671
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC2291540
- OpenAlex
- W2105046484
- Cited by
- 68
- References
- 49
- Citations by year
- 2025: 1 · 2024: 2 · 2022: 1 · 2021: 2 · 2019: 7 · 2018: 4 · 2017: 4 · 2016: 4 · 2015: 1 · 2014: 5 · 2013: 1 · 2012: 7
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institutes of Health (5p50gm065509-10)
- National Institute of General Medical Sciences (P50-GM065509, GM-65509)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- haplotype inference, hybrid cell lines, chromosome 19, African American ancestry, mixed ancestry, sample size, coalescent simulation, association studies
- MeSH terms
- Algorithms, Animals, Black or African American, Cell Line, Chromosomes, Human, Pair 19, Female, Genotype, Humans, Male, Sequence Analysis, DNA, White People, Mice
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